{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二周：Logistic回归和SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " -- 对Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集）进行分类器练习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 1)  字段说明\n",
    "    * Pregnancies： 怀孕次数\n",
    "    * Glucose： 口服葡萄糖耐受试验中，2 小时的血浆葡萄糖浓度。\n",
    "    * BloodPressure： 舒张压（mm Hg）\n",
    "    * SkinThickness： 三头肌皮肤褶层厚度（mm）\n",
    "    * Insulin：2 小时血清胰岛素含量（μU/ ml）\n",
    "    * BMI： 体重指数（体重，kg /（身高，m）^ 2）\n",
    "* 2)  DiabetesPedigreeFunction： 糖尿病家族史\n",
    "* 3)  Age： 年龄（岁）\n",
    "    * Outcome： 输出变了/类别标签（0 或 1，出现糖尿病为 1, 否则为 0）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn.metrics as metrics \n",
    "%matplotlib inline\n",
    "from __future__ import division"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 读取数据和数据初步分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果：无空值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 特征分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAExtJREFUeJzt3X+0ZWV93/H3BxAwSCTAhNKZIYN1qgujgt4QUmlXAJPwwzokAdQmkeg00zYmttFESW0x2qxVraZETGI7CcQhGoFG5YclppQfGhtBZwLyI6N1glBmgs6AMKIs0Ynf/nGeiYebZ+49w8y55zD3/VrrrLP3s5+97/fMuut+Zj/77GenqpAkabb9Jl2AJGk6GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdR0w6QL2xJFHHlkrVqyYdBmS9JSyYcOGB6tqyXz9ntIBsWLFCtavXz/pMiTpKSXJfaP0c4hJktRlQEiSugwISVKXASFJ6jIgJEldYw2IJPcmuTPJ7UnWt7bDk1yf5Ivt/ftae5JcnGRTkjuSvGictUmS5rYQZxCnVNXxVTXT1i8AbqiqlcANbR3gDGBle60B3rcAtUmSdmESQ0yrgHVteR1w9lD7ZTVwC3BYkqMnUJ8kifEHRAH/K8mGJGta21FV9UBb/jJwVFteCtw/tO/m1iZJmoBx30l9clVtSfL9wPVJPj+8saoqSe3OAVvQrAE45phj9rjAF//aZXt8DO17Nrzr1ZMuQZq4sZ5BVNWW9r4V+ChwIvCVnUNH7X1r674FWD60+7LWNvuYa6tqpqpmliyZdyoRSdKTNLaASHJIkkN3LgM/DtwFXAOc37qdD1zdlq8BXt2+zXQSsH1oKEqStMDGOcR0FPDRJDt/zh9X1ceTfBa4Mslq4D7gvNb/OuBMYBPwGPCaMdYmSZrH2AKiqu4BXthpfwg4rdNewOvGVY8kafd4J7UkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXWMPiCT7J7ktycfa+rFJbk2yKckVSQ5s7Qe19U1t+4px1yZJ2rWFOIP4t8DGofV3AhdV1bOBh4HVrX018HBrv6j1kyRNyFgDIsky4CzgD9p6gFOBP2ld1gFnt+VVbZ22/bTWX5I0AeM+g/ht4E3Ad9r6EcAjVbWjrW8GlrblpcD9AG379tZfkjQBYwuIJC8DtlbVhr183DVJ1idZv23btr15aEnSkHGeQbwEeHmSe4HLGQwtvQc4LMkBrc8yYEtb3gIsB2jbnwk8NPugVbW2qmaqambJkiVjLF+SFrexBURV/XpVLauqFcArgRur6meAm4BzWrfzgavb8jVtnbb9xqqqcdUnSZrbJO6DeDPwhiSbGFxjuKS1XwIc0drfAFwwgdokSc0B83fZc1V1M3BzW74HOLHT55vAuQtRjyRpft5JLUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpK55AyLJuUkObcv/IclHkrxo/KVJkiZplDOI/1hVjyY5GXgpg+c2vG+8ZUmSJm2UgPjb9n4WsLaq/idw4PhKkiRNg1ECYkuS/w68ArguyUEj7idJegob5Q/9ecCfAT9RVY8AhwO/NtaqJEkTN29AVNVjwFbg5Na0A/jiOIuSJE3eKN9ieivwZuDXW9PTgA+MsyhJ0uSNMsT0k8DLgW8AVNXfAIeOsyhJ0uSNEhDfqqoCCiDJIeMtSZI0DUYJiCvbt5gOS/ILwP8G/mC8ZUmSJu2A+TpU1buT/BjwNeA5wIVVdf3YK5MkTdS8AZHknVX1ZuD6TpskaR81yhDTj3XaztjbhUiSpssuzyCS/BvgF4FnJbljaNOhwP8Zd2GSpMmaa4jpj4E/Bf4zcMFQ+6NV9dWxViVJmrhdDjFV1faqureqXgUsB06tqvuA/ZIcu2AVSpIm4sncSX0g3kktSfs876SWJHV5J7UkqevJ3kn9++MtS5I0aaNM9/1u4E+ADwP/mMGd1O+db78kByf5TJLPJbk7ydta+7FJbk2yKckVSQ5s7Qe19U1t+4o9+WCSpD0z6pPh7gT+HPhkWx7F4wy++fRC4Hjg9CQnAe8ELqqqZwMPA6tb/9XAw639otZPkjQho3yL6V8CnwF+CjgHuCXJa+fbrwa+3laf1l4FnMrgjARgHXB2W17V1mnbT0uSET+HJGkvm3cuJgaPFz2hqh4CSHIE8BfApfPtmGR/YAPwbOB3gb8GHqmqHa3LZmBpW14K3A9QVTuSbAeOAB4c+dNIkvaaUYaYHgIeHVp/tLXNq6r+tqqOB5YBJwLP3e0KZ0myJsn6JOu3bdu2p4eTJO3CXHMxvaEtbgJuTXI1gyGiVcAdu9qvp6oeSXIT8CMMvg11QDuLWAZsad22MLhje3OSA4Bn0gmiqloLrAWYmZmp3alDkjS6uc4gDm2vvwauot0HAVwNfGm+AydZkuSwtvx0BrPCbgRuYnAtA+D8djyAa9o6bfuN7f4LSdIE7PIMoqretofHPhpY165D7AdcWVUfS/JXwOVJfhO4Dbik9b8E+KMkm4CvAq/cw58vSdoDozwwaAnwJuB5wME726vq1Ln2q6o7gBM67fcwuB4xu/2bwLnzlyxJWgijXKT+IPB54FjgbcC9wGfHWJMkaQqMEhBHVNUlwLer6hNV9VoG9zJIkvZho9wH8e32/kCSs4C/AQ4fX0mSpGkwSkD8ZpJnAm8E3gt8L/ArY61KkjRx8wZEVX2sLW4HThlvOZKkaTHXjXJvqqr/kuS9fPceiL9TVa8fa2WSpIma6wxiY3tfvxCFSJKmy1w3yl3b3tftqo8kad811xDTtXSGlnaqqpePpSJJAPy/tz9/0iVoCh1z4aiP5Nlzcw0xvbu9/xTwD4APtPVXAV8ZZ1GSpMmba4jpEwBJfquqZoY2XZvE6xKStI8b5U7qQ5I8a+dKkmOBQ8ZXkiRpGoxyo9yvADcnuQcI8APAmrFWJUmauFFulPt4kpV892lwn6+qx8dbliRp0kY5g6AFwufGXIskaYqMcg1CkrQI7TIgkrykvR+0cOVIkqbFXGcQF7f3Ty9EIZKk6TLXNYhvJ1kLLE1y8eyNTtYnSfu2uQLiZcBLgZ8ANixMOZKkaTHXndQPApcn2VhVfoNJkhaZUb7F9FCSjybZ2l4fTrJs7JVJkiZqlID4Q+Aa4B+217WtTZK0DxslIL6/qv6wqna01/uBJWOuS5I0YaMExINJfjbJ/u31s8BD4y5MkjRZowTEa4HzgC8DDwDnAK8ZZ1GSpMkbZbK++wCfHidJi4xzMUmSugwISVKXASFJ6pr3GkSSw4BXAyuG+zsXkyTt20Z5YNB1wC3AncB3xluOJGlajBIQB1fVG3b3wEmWA5cBRwEFrK2q9yQ5HLiCwRnJvcB5VfVwkgDvAc4EHgN+vqr+cnd/riRp7xjlGsQfJfmFJEcnOXzna4T9dgBvrKrjgJOA1yU5DrgAuKGqVgI3tHWAM4CV7bUGeN/ufhhJ0t4zSkB8C3gXgwcHbWiv9fPtVFUP7DwDqKpHgY3AUmAVsK51Wwec3ZZXAZfVwC3AYUmO3o3PIknai0YZYnoj8Ow2/feTkmQFcAJwK3BUVT3QNn2ZwRAUDMLj/qHdNre2B5AkLbhRziA2Mbgm8KQkeQbwYeDfVdXXhrdVVTG4PrE7x1uTZH2S9du2bXuyZUmS5jHKGcQ3gNuT3AQ8vrNxlK+5Jnkag3D4YFV9pDV/JcnRVfVAG0La2tq3AMuHdl/W2p6gqtYCawFmZmZ2K1wkSaMbJSCuaq/d0r6VdAmwsar+69Cma4DzgXe096uH2n8pyeXADwPbh4aiJEkLbJTJ+tbN12cXXgL8HHBnkttb279nEAxXJlkN3MdgplgY3G9xJt8d0nLGWEmaoFHupP4SnesEVfWsufarqk8B2cXm0zr9C3jdfPVIkhbGKENMM0PLBwPnAqPcByFJegqb91tMVfXQ0GtLVf02cNYC1CZJmqBRhpheNLS6H4MzilHOPCRJT2Gj/KH/raHlHbT5k8ZSjSRpaozyLaZTFqIQSdJ0GWWI6SDgp/n7z4N4+/jKkiRN2ihDTFcD2xlM0vf4PH0lSfuIUQJiWVWdPvZKJElTZZTJ+v4iyfPHXokkaaqMcgZxMvDz7Y7qxxncHV1V9YKxViZJmqhRAuKMsVchSZo6o3zN9b6FKESSNF1GuQYhSVqEDAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKlrbAGR5NIkW5PcNdR2eJLrk3yxvX9fa0+Si5NsSnJHkheNqy5J0mjGeQbxfuD0WW0XADdU1UrghrYOcAawsr3WAO8bY12SpBGMLSCq6pPAV2c1rwLWteV1wNlD7ZfVwC3AYUmOHldtkqT5LfQ1iKOq6oG2/GXgqLa8FLh/qN/m1vb3JFmTZH2S9du2bRtfpZK0yE3sInVVFVBPYr+1VTVTVTNLliwZQ2WSJFj4gPjKzqGj9r61tW8Blg/1W9baJEkTstABcQ1wfls+H7h6qP3V7dtMJwHbh4aiJEkTcMC4DpzkQ8CPAkcm2Qy8FXgHcGWS1cB9wHmt+3XAmcAm4DHgNeOqS5I0mrEFRFW9ahebTuv0LeB146pFkrT7vJNaktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkLgNCktRlQEiSugwISVKXASFJ6jIgJEldBoQkqcuAkCR1GRCSpK6pCogkpyf5QpJNSS6YdD2StJhNTUAk2R/4XeAM4DjgVUmOm2xVkrR4TU1AACcCm6rqnqr6FnA5sGrCNUnSojVNAbEUuH9ofXNrkyRNwAGTLmB3JVkDrGmrX0/yhUnWs485Enhw0kVMg7z7/EmXoCfyd3Ont2ZvHOUHRuk0TQGxBVg+tL6stT1BVa0F1i5UUYtJkvVVNTPpOqTZ/N2cjGkaYvossDLJsUkOBF4JXDPhmiRp0ZqaM4iq2pHkl4A/A/YHLq2quydcliQtWlMTEABVdR1w3aTrWMQcutO08ndzAlJVk65BkjSFpukahCRpihgQcooTTa0klybZmuSuSdeyGBkQi5xTnGjKvR84fdJFLFYGhJziRFOrqj4JfHXSdSxWBoSc4kRSlwEhSeoyIDTSFCeSFh8DQk5xIqnLgFjkqmoHsHOKk43AlU5xommR5EPAp4HnJNmcZPWka1pMvJNaktTlGYQkqcuAkCR1GRCSpC4DQpLUZUBIkroMCO1zkvxGkl9ty29P8tJ5+t+cZOTnHSc5PsmZe1pnO9a9SY6cp8/Xd/OYf/f5pT0xVU+Uk/a2qrpwDIc9HpjBpx9qH+cZhPYJSd6S5P8m+RTwnKH29yc5py1fmOSzSe5KsjZJhg7xc0lub9tObP0Pac8j+EyS25Ksanebvx14Rev/il6/tv/zWtvtSe5IsnKez3BVkg1J7k6yZta2i1r7DUmWtLZ/lOTjbZ8/T/LczjFfn+Sv2s+//Mn962qxMiD0lJfkxQymCDkeOBP4oV10/Z2q+qGq+kHg6cDLhrZ9T1UdD/wicGlrewtwY1WdCJwCvAt4GnAhcEVVHV9VV/T6JTkE+NfAe9pxZxjMlDuX11bVi1vf1yc5orUfAqyvqucBnwDe2trXAr/c9vlV4Pc6x7wAOKGqXtDqkUbmEJP2Bf8U+GhVPQaQZFdzSZ2S5E3A9wCHA3cD17ZtH4LB8weSfG+Sw4AfB14+NJ5/MHBM57i76vdp4C1JlgEfqaovzvM5Xp/kJ9vycmAl8BDwHeCK1v4B4CNJngH8E+B/DJ0IHdQ55h3AB5NcBVw1z8+XnsCA0KKQ5GAG/8Oeqar7k/wGgz/kO82ec6aAAD9dVV+Ydawfnn34Xj9gY5JbgbOA65L8q6q6cRf1/SjwUuBHquqxJDfPqm92bfsBj7Szk7mcBfwz4J8zCKvnt/m3pHk5xKR9wSeBs5M8PcmhDP4Yzrbzj+2D7X/f58za/gqAJCcD26tqO4MJDH9557WKJCe0vo8Chw7t2+2X5FnAPVV1MXA18II5PsMzgYdbODwXOGlo235D9f4L4FNV9TXgS0nObT8rSV44fMAk+wHLq+om4M3tZzxjjhqkJzAg9JRXVX/JYAjmc8CfMpjCfHafR4DfB+5i8Ad9dp9vJrkN+G/AzhlD/xODaw53JLm7rQPcBBy38yL1HP3OA+5Kcjvwg8Blc3yMjwMHJNkIvAO4ZWjbN4ATk9wFnMrgIjnAzwCrk3yOwXDZ7EfF7g98IMmdwG3Axe3fQRqJs7lKkro8g5AkdRkQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSp6/8D+kX+gs0EATgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#糖尿病为 1, 否则为 0;图示显示非糖尿病人比糖尿病人大概多一半的人数\n",
    "sns.countplot(data.Outcome, order=[0,1]);\n",
    "plt.xlabel('diabetes labels');\n",
    "plt.ylabel('num of diabetes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter of Pregnancies')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pregnancies 散点图\n",
    "plt.scatter(range(data.shape[0]),data.Pregnancies.values,color = \"blue\")\n",
    "plt.title(\"Scatter of Pregnancies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出特性：\n",
    " 怀孕次数有不少为0的情况，可能存在女性终身不孕的情况,不好判断是否数据异常."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter of Glucose')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Glucose 散点图\n",
    "plt.scatter(range(data.shape[0]),data.Glucose.values,color = \"blue\")\n",
    "plt.title(\"Scatter of Glucose\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出特性：\n",
    "    Glucose 存在为0的数据，根据其物理意义,明显不对,将对他们填充样本的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter of BloodPressure')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BloodPressure 散点图\n",
    "plt.scatter(range(data.shape[0]),data.BloodPressure.values,color = \"blue\")\n",
    "plt.title(\"Scatter of BloodPressure\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出特性：BloodPressure为0，违背其物理意义，用平均值充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter of SkinThickness')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WF1UXFlRFis/FRfv+odK1vAsLqoVqOXxbWMibDwmD7TLdAFjSCJ1LH38H8C1eT4bzCMOE7TLd0Mc/ILp4Mnfa/bDLZT5kuTGJdpn2vrscoeLvgNxP5i4HP+x554XtJ5Oh6/md5dB3lyNU/B2Q24paDmv7fPazYfuniWm2aOvvn5ifB446Cnj1q/OUZRr67jS3XzQxEwO5t2me3G1icVF19erDJ8tWr46fMBVpnoATySt3lyyHMjSRu637pIuyDL3dp739EDm527vS12Wo+FXzRsksh8iLIZchpa1yl6vPaLAu2miS7R5Td330y5xtTMW/jJl2q0R1uGVIlSunRdt3HXVhnU+qTLH5THpEkrs+qPgr+NxRGWc/eYZYhlSLL6fF2PeoKCV/W9vGtHvoObGyT7rOc+dHxW/wuaP2bVmR4ZBq8eXsS337w2PLkvt6ikkvtu4mrQtytzEVv8Hnjtq3ZdU1Q7Ssh0qOvtBU3+U+QHV29lCatrYYQr/M6SefnY3rezH10HYOoLptW/t5i4uq8/OHjp2fb5c5x3VFi78jxe9zR+3bsuoSjmbC6KK+mtL0SXta267teoqVP+b6tNV5m/IPqe9cbUMff0bFX70Tl9bVWC3+aS5bXyOV3PnarM96W9Tz3rYtr698EviUN0T22D68uNguQ9NNIySfnNcVo3oyKH7Xnb7pjjqtlpUP0zqaWU5tYrOAq23Rh8XZBb7X4CRGPjYZ6ueHXCtDva5Gq/ht/sXlFNVT4pK7rT7m5w+dNz9fbEMoe9UXPq0jlTq+Fn9fFmcXLC62j7Z9RuFN6cVcn215NeU3qfrvUteMVvEP9U7cBbERSytXqq5a1X5B9GU5+liK09iOvj7+5WBxVmnrn5Ns223b/PObxIir65HaaBX/0C2hnPiWtW5hVKMWhmRZuyzjULlSLatt2w5ZjLOz9mgQX1mqFm9dpmmw+EPrtOn4Sct+5JH++YWUL2fEU66yj1bxD9n3mZtYq8/lc+7LcnTJFdKOqf2gzVJMUf45Ze6jnw81ksWV18qVT27HVav60Qldj9RGq/hV81glsWmlEpJfm+VeWg9tMcm5Letc5bPJFRoDnmpZ2fzTXZJicdoigHLQdSRL06godf7JNsfVB7T4O1T8Idisj0lbVaEWn82Ssf2/bZvd19ql9WUrX2j4nY1Uy8p2Uxwik+irXVqrPvM7MWUa2lwIffwTVvxtlpTtDmyLEOpCMebw8c7MuK360opq+6+rEY9P+WxyhcgxaYt/UqPGmH6cK/8urVWfUWhMfkOZ86uPvm3XXApU/BVsd1mbRZD7CUQXOaI6Stl8LyJbuXJbJz7l841CcskxSR//pEaNtrRcbdp1/qn4zDvZroc+ZA6RoWn03YVMvSl+ALMAvgXgGvP7VADXA9gN4AoAq1xp5Fb8MVa97T9fqyHU0sph8ZfbzIz/hdSWX25rKXcUUo76b4vcWVxUnZs7vD7blL4tLj1XHdrymZ+3x6un5l+tx6rPfX7+8DrysVxDRyxt5bW166TnPmzlCilbjlFIn4r/TQA+XlH8nwRwgfn+AQDbXGnkVvw2SzPUWquf30aMpZEa1VHfQpVBvVy2Y2KItb668tO2WfWbNvnJ6dM/csju6/+OaeOYvFevLuouNFom5VorN9fob4jRTr6jmRzzDr0ofgAnA7gOwIsBXANAADwEYIX5/2wAn3OlM0mLX7XdoimthTbr2XaHDrX0miIafKI6bMq97su3+fabZGxLu6ybLiJPmiynrvzXMTfGUCvVNlfgK2tqPm19IyXvkCdiXWm1RaEBxYiiej26Rn+2KJ6cczDVc1xPIi97ix/AlQCeC+BFRvGvBbC78v8pAG5uOXcrgCUAS+vXr0+vgQq+VkCbf7mpYV1xwCGWXoqVEhoJY3uSsZ6vrzLMFWtusy5D9vsqVN/ytZU3xC8dUm91fPPJZZnH5O3qd7a0QtYqcqURUlexczC+o5NquVw+fiDPMyITV/wAzgfwv8z3YMVf3bqY3PVZYzvUz2gjh7/e1wII8YHbyhjrm8xVLpd1WR8JdRW541vekPoJqbeQNqv3yar/vfweM2L1bZMc7RsypxSbRmgbxozmbWksLrrn3abS4gfwXwDcA2APgPsBHACwq29XT8gdPWdkQY58fX1+IREoqSORkPoILZdP/ceubRNSb00+/qY8cvjefWQNyadtJJQz7y58/L7t6UojtM/G9KHQfup73tT6+J9IxFj85vunapO7b3CdP+k3cNmsx7bN5+7s6zsM8UuG+sBj6qOtDK75hJRy2Y5vSyPFki2pRvVUzy/nIlzt7xuB5LsiZVufCemjvhFdvn24bbTsO5L2KZ+qf990XVchUWE5Lf5yBeCmkVeu9nAxJMV/GoCvm3DOTwE4wnX+JN/A5bIQYmLIQ2mbW6jn2yZLm+y55xNsFkuTpedbrtDIDtuWc+2YnFatz3xEio+7i3qadIRMV/mFRhOlRN/F9uGpj+PPsU3K4ndFxdis7NzUI4tC4vBD1jePsdJKXBZnW36+1ld5fKzvvYzDD43eCLXSZ2bsfcFltbed62N9+lqPbeUJ6cOpcyi+1Pt+7No8tvq1/eezCmtdxvozHmW9xLRPTp1CxW8IscCq25DW8vC1GOq/fSzwEEvDJZtPnfn4VFOs2lDLzhXl5JNfrovWZ3TqEx2SS86unp+oksvKj03H57wcI9FJ6ZjRKf6qD7Qe/dH0n8uqdFk1XY0CYiJFqqtu2uSx+dx9sVnkdd99k/WdErnh2mwjn1Cr3qePuPpKaB/JGbESurZRiDzVayuVXKMKW/vaRl4p7+RO3XKPnFRVR6X4bXfkGN9bzidsQ7FZu7HPFPikHTqk9vHdN9VrrK/b561huS/MlOiY3L5jV/t10Rdd10yOfHI92exbLzFtmmtepYs2qjMqxe/je/a9a1efqKxbbL6RHr6EROiUctl85LE+5Byyh0Qu+FjCTce4fMExlpnryVYf67BaLlddp4wkbSO2GOs+pB1S27ONHBZ/SLvHjOJC+1XT+6yreXc1T6iq41L8PvGxobG3MX691PVXfCxiWzl8LOkcsse0Qa58bMS0WcjTvyEWcBf+8ZwjzdC0Uvpd12XKbZGn+Pi7jHryYVSKP5fFX41wifXr+d7NYy1324igLT3ffEtiolJ86yvWr+myKmPmcUqrPuTduj4WsK0+fOrZVu7QlSbb8nCNLOuEXg8h7Zw6X5bDB++KePKN6mmTvas5wTqjUvy+Pn6fiIjy+NTIEpcvt+3c2CeCfdNzWVixIxGf+u3KOg2tk3q/iLE4XRa962nq2GiSXNa0y4JvurGGjKYmGRXne23n6peh7dLlnGCdUSl+1cMtsWqnrlrxvn7omPXN65vNgrBZKD4RNiFzA02Wl82ij13XfXHR/ezBmjVxlmvOaJeyf5TzAyHPQPjIVLa76xkAV762trDJ5jMnUfqgfdq1mmZZvuqnrf+HKtNQi9hmhc/PH97HQlZJtcni2y6xc0OpjE7xl8RaOTFbrN8v9AnY1HLHnu+rRGPP9ZXVZV2HtKtP9FE17dz1laM+22QLkcknusXWr1Lb1CV37sg63zkXW7q+7RLaN3Ja/6NV/DbrMMUX2PZUaIxv2zXyqFp9If7dFD9ibN3MzqY9TdpWR9XytB3vY7nW08+5bkpI+4fIGGuNh7Zbzvqy5ePqhzGRPb7n+M7JuNL1qbPYEWi1P8c+tVwyWsVvu7OnWGmpVlbI05e+sk7iqdFJbtU6dpXf13Kvpx0a3eUip+Uf6n/vatSRe3PVZ0wElM85rvppkiv2Ooh53iJnPywZreJ3xToD/n7K6mazwqtWUZuf2+X7jd1io2R8622Sm++oKHQto2rarnI2rV2UI5qpbWta98fH4u9itNHlZpu7ymnxh9RPU1vH1Gl1zsg2Sop941sIo1X8TXf50FUt61vI07+hlmjqlit6Irf1GFoPdUvWdWyILNV5k1Ar0OVLTrXu2lY19YmQmlQfy7XljHrxacfQfhdzHYSsouurQ6pb6PU9SsXfFH0QcwdOXS++qgxCLdPQLZfFX9ZfrE/X9e6ApncZt1nRNhlC69NmwfvUqavNc4zgmtrQpx1i8raNSLvsp66+GvIshW87xq615Eq3Okpra6e5OXtkUMzcoA+jU/wpMctN5zTRlR88ZYSQMxQs1urPOdfgsvZDNpe15OMnnsTcR5OcufO1RZzEWqO52iM1Ii3k+g6Vy/U+Blu6Lvlj5iBcjE7xx0TzVH2BKfHkqVtpHcSeFxv7nMNf7bP+iE8ddxUhY6sHn5U5fa3GlDmcWIs/NkopNk7dpx/EtIfrmYjY67LehrZY/rZ8XE+6x7x7uIptVMyoHg9Co3li7qZdWkRtb3uybaFvBnPVQ2oZfJ/4TPWrNm0rVvjXRRftmDOG29fHH2Kl+7hOUupn06bwcpakRlr5RsD5PLvg+8xN2d4+fWKSjE7xuyIDQix727FN72jN5eet+9ht7+uMed+sT8RT6laP3vCJ2MiRd8gKlV2M3Nosat+nNn3PKftbWzRZ7JPIdULkKdNv66vVqJeUVVVtlrmPvPXryecaso2oFhaKp9F9+masvglldIq/S6t+Ej7QUP9naDqqaX7jkHmIEIspVS6fcueqh7Z6T12nJaZftfmec9RPUxlinz3xeY+C7xr5vnWeqy/5lNt3a3siP5feOlT2kSl+1XBfcpN/OmauIMfWFudcHwWkWHYp8ov4R9T4WPO+Fn/dQvRZXdRFTD00jchK+Xwm8Wz9MlSemLeGhc5n1Yl92tx3riJ0hGFr8xwjcNtoO3YLmcuJjdYbpeJ3YbOsXBFAIt1GeLTFc+dc86PrqI1qXdnybLJ4XdETIenF9AObZWob8eWIaArpVz7t1yRjyHsHQmVM9d/XRyO+70FuG8V03b9zXBeq9gi22BEaFX8DKTHZOS1+X/+8T351v6fLqouxyEK3trhom6UZ8n6C0DXpm2iSqWn+xnc0mDKH1OYnrkej+PjE22RJsSxtkT6utXh8+nB9tBDbz1T9bxp9bCHXNy3+jKSuw5HDYvZZETFE3lDruk7uUUCsBZzrWYtYfOouh4y+9W1bpTVkhFQe79N/fOslpB1c5Y29tprytin9XL762K2pbWz9iT7+jKT4dkva5ghCfJk266meh0+aPuWzWaFNFrUtMsJnlULfjusqa64oFRc+cxGho6MmGX3b1fVehqYIsLY6T7Es286tr0/jMi6a5ql8RlMh/cvWL22jHp8oqjLf+mjT9R7uprZx9Xmfd3K0QcXfQIx1G6LAfKNtXPnFrjPiGiGkrjrp8yR0SF4+1qBPfebAFX0UE+GR8kRuzvKlWJa+8qZGNuXIx3aeTQ6fZ2hSn5GxHRd7jTbXwQgVv48vuMliT336rpq/j4UaY+E2bXVLwnfUEVufodZZrDVZnuvrz86BK5+YuZCmuPNcdZejbGUIZmy9+MqcGtnlm0/btVN3hzaNlGwjAt82qacxNxf2zILPU/AuRqf4fSxlm9+17ZxQy8s3iiWXX73qI/W1SFPxld9Vdz4ydm0hhZYpRzvlftLWp2xtfcP1xrfQkU4TPs9yhNR/W79q8/GvWHGojCmjj5T5kDKf0P4SyugUf6ollRpPW7WOfdbcCI1gyBVxVM4jpJan+r9tPsAW7eKa6wipqxzWf2ibhLZVOQKo1mfo+1td/Sl0zRmfPu472mnrW2112haR5urvtid42yKkyjKmjD5SRrAxW8yob3SKP9V3GhMRk+PcEH95rmcMYspjK1ubVTg7a19PKFTGnHMYNmIv1DL/kGN95hZiRpAhPnSXJZvat0JG46nRPa76jB19+PSt3M/5xMzzjE7x5/Cd+swRhOTtc8f2tYZsx/pE2uSQqSkdH99o27mh9eYjV5s16GM1l8SuTlmmGdIWrjrIZaF2EdkTe23ZRjm2vpQyfxba55r6i6sPxVj8tndW0+L3IMXH75te2/mxPsHQfJqODXnTlcuf6ypPvWwxkS7Vegm1rGKtwdB8QstTTyukHlyypax1VLdiQ2L/6/WeGvVSJcYyto1qQ6z1WGu+q7k7nz4QwsQVP4BTAHwRwK0AbgHwu2b/8QCuBXCH+TzOlVaXUT3j5fNdAAAQWElEQVS+uCyDHFEAbXLbLNV6mWwrIs7NHfo9P98eg+zrd66XLdanGeq/bqqrkLwmObLwPb/el9rqIKdPui2ixQfXHEHIvINtlGurb9/rzNWnQvucrfz1J5frc2PVazC2D4TQh+I/EcCZ5vvRAL4H4AwA7wZwsdl/MYB3udLqKo4/BJt1kdsCaiNnBELOqBXfeH7bu0dT8S2Pr6UYkrZPTLdvHcaUM8bHn0qOOnSNwFL6Ype42jP2Ke0uZO/d1QPgKgAvAXA7gBPNvhMB3O46N6fij/HVqdqtKJdFV66XnnoHz2ntxfofbVFKPmnmeKtQHd/oj/J/m7XaFjZZt9xCyxBjIbvKGxvV43sNpIw86ue6/NZNI+bQ9ynn6k9N9RQ6Am6SxzWv0sUNq1fFD2ADgH0AngLg4cp+qf6unbMVwBKApfXr12epBF+fuO8TdqFPrqbe3X2srLbY5bpCi/WruurXx8ef07KJGbm45kFyxszb5JyEdeojR1N9uK6LUAve1adi54d8+2aOeorZQudnctOb4gewBsANAH7V/H649v+PXGnksvhDfcG+cwQ+Fn/bf03rlITKHxKtUeYR8+SpzxxF3XKzrTyaw48ZO69gu/H5PNsQI3vTyMR1bi5fb+hciK1uqso/5npo6lO2c6rzJyGrtvrOD4WMaGI2VxlT1uJx0YviB7ASwOcAvKmyrzdXT6iVGxJdY3sSMiQ/lxJIXePEdy2SUNli6rzLUVDqZiPFeg85N9coIed8jo8Mvm3iYw3X26OtTkLfL+Cq25z9yjWq8Y2ui6GPyV0B8FEA763t/6+1yd13u9Lqw+IPXQWyyUdZfVLVN1+XVe3yxfr4H9uOmZtL82OH1HlI/cZYZmVZYi7UqsXflHdoVJBPfTSdG5NP0yg1xxuoQmRok7uqTOsjXZfFH1vGmZmwfhPyvunqnJdtPqI6jxHzbuwU+lD85wBQADcBuNFs5wGYB3CdCef8PIDjXWnlUvwhPv62Rozxx8X4PLtIO3fesfKF1K/LMnNZUTH1U/r4U2VvIsTPmzv6KOdmK2uoHKXFbjsmR145LfqQkbZPP+zKz997VE/K1kdUT4pV15avbzx8CKEWte2/at71EUxIjHdT2X0tNN/oo+pxPhEjbZbW3Nzhq7JWJ3ZzjFbq2NL09ZW35RMbqRWj+EJGpr79PuaJ1dAy+8wpNG2uEbDLf+/Kb9lY/Dm3PuL4u4jCcEUu5I70ifWHdumLtFk+TeXP8aRqbHlsF2pOH39bPYT2wRArNiQiLbWfhjz5HVqvofLbnhJ3nRParr4jjC6ju6j4LbT5kH2jekLyKDtS2ehtUT2+EQquKJ42mWMjGuqjApeM9WN8Y+hdfl+fSKbUaAqbpZoyB+JrDYf2uRjr1/YE6qZN9ncO++IjV3V0FnKNxVr8TXnlXJHX95pyvaM4FSr+FnytjElFcoQe77JcckfK2Cym1LVKfOPAQ59UjY2f9pU3BZ8oLN88Ynz8bXNcoVEyKXKl1GOsj983rS6fLJ/EcxxU/C34+lEnFclhO9610mRbh881b1BNy6dMMT7nhQV3/m1lb4uNj227Li3VmDx8sI1Sbe9JyD3HZZPD9T6HlBFUNe2YUVmKHH2m3QYVfwu+1mDKU3eh5/o+CRzjD/fBxyee4nu3bSL+5eh6tOZjTcb6pkPzyEGXEUWuMuUa4ZIwqPhbyGHxuyKFQqNAfC3luo861UpTbZ+LqEf19GHxp4zC2qxCl+XlGk3leD+zK48mi7xephR/eJOcQxjh1q+tLtZ5SqUPKz4EKv4WUq3GJl+oz5r4oRZQ2xbi685VF77H5vTx5xrhdGn9h8jhm0dOH3zu9m0j5wi3q7mBHEzDSIWK34LvXbvpuBCr1rZmuy2v1LXHfcsXaqn5pLu46PdMQZvv3pa2LVonNLrCdk6TTF08k1HNo1SUrnoLzTPEQs09bxEzpxVax5O0wHOMsLuGir8jQvzYsb7axcX4NEOsklxzBCkypKbrWlXSt71yxI/HrvLZNsfSZR/LSS4ff2h5J22Bd3W95ISK3wObtdD2X4jFn2IJxMYYT8qv66IrS6ye7po19jLkaq8uo3FS1lsairXpam9b1E/IaNpnTilXndRlzvlu3K6g4ndgsxZC/+viTVOx1kyIVTINPksbPiOjEF996FxBrOUXMxeSM85+0qTMq9nO6dICn9R1nhsqfgc2a8Hmg2zzoXdh4cak2YXfvmtiZHDNJVTLm8tyc1mlvpZf6DxRWR/VNY/q6wwNma6isWIt/q7mk4YAFb8Dm7XQ1zobOZg2Kz5GXh+LOVe0Sxfn+847VJ+jmLZ2rTKkuaQuI8iGABW/gxiLP9Sy64shWPG+xFhtrvbxecNRah2lnN8mf+pzFENlSHNJvrJMa31T8TsI9eNP011/moixrKZ5RKYaZ6lOqwWqOqzRSu6nxIcGFb8HrqieoUdSLAdyWvxdrnyYexS1uBj2/oNptUBLhjIKjZ1vGPrIuYSKPwPTetefJrr00/YpY+402RfzsNzrkYo/E9N41x8KvnUXG9UzzU9sxqQ5lMixaWc5lzlW8Utxbr9s3LhRl5aW+haDJLBrF7B1K3DgwKF9q1cDO3cCW7b0J1cMMzOFWq4jAjz++HDSDGU5tREpEJEbVHVj6HkzXQhDxsf27YcrFKD4vX17P/KksH592P6+0gxlObURSYOKn2Rh376w/UNmx47CEq6yenWxf0hphrKc2oikQcVPsjAEizYXW7YU7o+FhcIVs7CQ7g7pIs1QllMbkTTo4ydZoP94+LCNlh/08ZNeGYJFS+ywjUgJLX5CCJlSaPETQgjxgoqfEEJGBhU/IYSMDCp+QggZGVT8hBAyMqj4CSFkZHSi+EXkZSJyu4jsFpGLu8gDKB5IWbu2iEkO3WZni8+ZmSf/t2ZNsaWkdcQRzceuWOFOzyf/tWuBN7wB2LAhrvxVuUuZNmwo6nTXrub8m+qqmk61jJs3h7VNeU5beVasKMpbb/M1a+L7QL08GzYcqtOZmcPro5SrrQ7K/8oyzMwUcpWy2c5bu9be3m31W8rU1KdmZ8PboL5t3nzoOvMpf+xW9uVYWZ/xjGb51q4tZAeK9FNkLPtZ2S9s8tb1QL39mspfyjkxYpb0tG0AZgF8H8BpAFYB+DaAM2znxCzLvLiounJl81K33OK3lSv93xHbxzZp2VauLN6F23e5+9rOOMP9vuMhb6tWqW7a1L8cPnLGLBeNyGWZu7D4nw9gt6reqaqPAfgEgJfnzmT7duDgwdypkoMHi644VCYt28GDwGOPTTbPIXHrrU9e0XOaeOwx4Lrr+pbCzWOPTXaV1C4U/1MB3F35fY/ZdxgislVElkRkaf/+/cGZcEVBQshyYpI6rbfJXVXdqaobVXXjunXrgs/nioKEkOXEJHVaF4r/XgCnVH6fbPZlZccOYOXK3KmSlSuLCaehMmnZVq4EVq2abJ5D4owznvwegWli1Spg06a+pXCzatVk383QheL/BoCni8ipIrIKwAUArs6dyZYtwIc/DMzPx50/Y0repEjm5ootJa02ZTE7607PJ//5eWDbtmKFxVhKuUuZFhaKOv3Yx5rzb1O6M7VeNDtbXGwhbVOe01ae2dmivB/72OHpzs3F94F6eRYWDtWpyKH6uPTSQ3LZbjwih8ogUshVymY7b37e3t5t9VvK1NSnZmbC26DOpk3ALbccWtET6ObGW/blWFnPOKNZvvn5ou0+//ki/RTKflb2C5u8dT1Qb786pZyTXCW1k9U5ReQ8AO9FEeFzqapa72VcnZMQQsKJXZ1zRRfCqOpnAXy2i7QJIYSkwSd3CSFkZFDxE0LIyKDiJ4SQkUHFTwghI2MQ79wVkf0A9kaevhbAQxnFyQ3li2fIsgGUL4UhywZMj3wLqhr8BOwgFH8KIrIUE840KShfPEOWDaB8KQxZNmD5y0dXDyGEjAwqfkIIGRnLQfHv7FsAB5QvniHLBlC+FIYsG7DM5Zt6Hz8hhJAwloPFTwghJAAqfkIIGRlTrfgn9VJ3hwyXisiDInJzZd/xInKtiNxhPo8z+0VE3mfkvUlEzuxYtlNE5IsicquI3CIivzsw+Y4Uka+LyLeNfO8w+08VkeuNHFeY5b0hIkeY37vN/xu6lM/kOSsi3xKRawYo2x4R+Y6I3CgiS2bfINrW5HmsiFwpIt8VkdtE5OwhyCcip5s6K7dHReSNQ5CtIuPvmWviZhG53Fwr+fpezIt6h7Ah4qXuHcnxQgBnAri5su/dAC423y8G8C7z/TwAfwNAAJwF4PqOZTsRwJnm+9EAvgfgjAHJJwDWmO8rAVxv8v0kgAvM/g8A2Ga+vwHAB8z3CwBcMYH2fROAjwO4xvwekmx7AKyt7RtE25o8LwPwOvN9FYBjhySfyXcWwP0AFoYiG4pX1d4F4KhKn7soZ9/rvGI7rJyzAXyu8vutAN7akywbcLjivx3Aieb7iQBuN9//EsCrmo6bkJxXAXjJEOUDsBrANwG8AMUTiSvq7QzgcwDONt9XmOOkQ5lOBnAdgBcDuMZc+IOQzeSzB09W/INoWwDHGOUlQ5Svks+/BvCVIcmGQ+8tP970pWsAvDRn35tmV4/XS9174gRVvc98vx/ACeZ7bzKb4d9zUFjVg5HPuFJuBPAggGtRjOIeVtV/apDhCfnM/48ASHjHlJP3AvhDAI+b3/MDkg0AFMDfisgNIrLV7BtK254KYD+ADxtX2SUiMjcg+UouAHC5+T4I2VT1XgDvAbAPwH0o+tINyNj3plnxTwVa3IZ7jZkVkTUA/grAG1X10ep/fcunqj9X1WejsK6fD+AX+5KlioicD+BBVb2hb1ksnKOqZwI4F8BvisgLq3/23LYrULhA36+qzwHwUxTukyfou+8ZH/mvAPhU/b8+ZTNzCy9HcfM8CcAcgJflzGOaFf9EXuoeyQMiciIAmM8Hzf6JyywiK1Eo/V2q+umhyVeiqg8D+CKKIeyxIlK+Ha4qwxPymf+PAfCDjkT6ZQC/IiJ7AHwChbvnLwYiG4AnLEOo6oMA/jeKG+dQ2vYeAPeo6vXm95UobgRDkQ8obpjfVNUHzO+hyLYZwF2qul9VDwL4NIr+mK3vTbPin8hL3SO5GsCF5vuFKHzr5f7XmCiBswA8UhlaZkdEBMCHANymqn82QPnWicix5vtRKOYfbkNxA3hli3yl3K8E8AVjmWVHVd+qqier6gYUfesLqrplCLIBgIjMicjR5XcUvuqbMZC2VdX7AdwtIqebXZsA3DoU+QyvwiE3TynDEGTbB+AsEVltruGy7vL1va4nT7rcUMy2fw+FX3h7TzJcjsIPdxCFlfNaFP616wDcAeDzAI43xwqA/2nk/Q6AjR3Ldg6K4epNAG4023kDku9ZAL5l5LsZwB+Z/acB+DqA3SiG4UeY/Uea37vN/6dNqI1fhENRPYOQzcjxbbPdUvb/obStyfPZAJZM+34GwHFDkQ+F++QHAI6p7BuEbCbPdwD4rrkuPgbgiJx9j0s2EELIyJhmVw8hhJAIqPgJIWRkUPETQsjIoOInhJCRQcVPCCEjg4qfEEJGBhU/IYSMjP8PTAacDynrn0cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SkinThickness 散点图\n",
    "plt.scatter(range(data.shape[0]),data.SkinThickness.values,color = \"blue\")\n",
    "plt.title(\"Scatter of SkinThickness\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出特性：SkinThickness 为0，违背其物理意义，用平均值充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter of Insulin')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Insulin 散点图\n",
    "plt.scatter(range(data.shape[0]),data.Insulin.values,color = \"blue\")\n",
    "plt.title(\"Scatter of Insulin\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出特性：Insulin为0，违背其物理意义，用平均值充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter of BMI')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BMI 散点图\n",
    "plt.scatter(range(data.shape[0]),data.BMI.values,color = \"blue\")\n",
    "plt.title(\"Scatter of BMI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出特性：BMI为0，违背其物理意义，用平均值充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter of DiabetesPedigreeFunction')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DiabetesPedigreeFunction 散点图\n",
    "plt.scatter(range(data.shape[0]),data.DiabetesPedigreeFunction.values,color = \"blue\")\n",
    "plt.title(\"Scatter of DiabetesPedigreeFunction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter of Age')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Age 散点图\n",
    "plt.scatter(range(data.shape[0]),data.Age.values,color = \"blue\")\n",
    "plt.title(\"Scatter of Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出特性：人群年龄已中青年居多，成正金字塔型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 异常值处理\n",
    " 对出现的0的数值进行处理，这里都用平均值填充\n",
    " * 正样本对应的0特征数值,用正样本的均值填充\n",
    " * 负样本对应的0特征数值,用负样本的均值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "经过处理后,特征Insulin的0值数为: 0\n",
      "经过处理后,特征Glucose的0值数为: 0\n",
      "经过处理后,特征BloodPressure的0值数为: 0\n",
      "经过处理后,特征SkinThickness的0值数为: 0\n",
      "经过处理后,特征BMI的0值数为: 0\n"
     ]
    }
   ],
   "source": [
    "positive_data = data.loc[data[\"Outcome\"] == 1]\n",
    "negative_data = data.loc[data[\"Outcome\"] == 0]\n",
    "\n",
    "positive_sum=np.sum(positive_data)\n",
    "negative_sum=np.sum(negative_data)\n",
    "\n",
    "# 需要进行对0异常值做处理的特征\n",
    "Zerofields = ['Insulin', 'Glucose', 'BloodPressure', 'SkinThickness','BMI']\n",
    "for i in Zerofields:\n",
    "    #求平均值\n",
    "    positive_avg=positive_sum[i]/len(positive_data.loc[positive_data[i] > 0])\n",
    "    negative_avg=negative_sum[i]/len(negative_data.loc[negative_data[i] > 0])\n",
    "    \n",
    "    # 用均值对是0的特征填充\n",
    "    for j in range(data.shape[0]):\n",
    "        if data[\"Outcome\"][j]==1 and  data[i][j]==0:\n",
    "            data[i][j]=positive_avg\n",
    "        if data[\"Outcome\"][j]==0 and  data[i][j]==0:\n",
    "            data[i][j]=negative_avg \n",
    "# 检验            \n",
    "for c in Zerofields:\n",
    "    print(\"经过处理后,特征%s的0值数为: %d\" % (c, len(data.loc[data[c] == 0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 特征相关性检测\n",
    "* 检测特征之间的相关性\n",
    "* 检测特征与响应变量Outcome的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns\n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data.corr().abs()\n",
    "plt.subplots(figsize=(15, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('Outcome.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'corr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-c1dd58187438>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcorr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Outcome\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutcome\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'corr' is not defined"
     ]
    }
   ],
   "source": [
    "corr.sort_values([\"Outcome\"], ascending = False, inplace = True)\n",
    "print(corr.Outcome)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述的结果可知:\n",
    "* 1.年龄与怀孕次数有较强的相关性\n",
    "* 2.血浆葡萄糖浓度与\"是\"糖尿病这个响应结果存在很强的正相关性\n",
    "* 3.体重指数,年龄和怀孕次数也是影响糖尿病的比较重要的正特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 数据分割\n",
    "根据要求,训练数据80%,测试数据20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分割出来的训练集数据占比79.9% (614)\n",
      "分割出来的测试集数据占比20.1% (154)\n",
      "分割出来的训练集数据正负样本比率34.7% : 65.3%\n",
      "分割出来的测试集数据正负样本比率35.7% : 64.3%\n"
     ]
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = data['Outcome'].values\n",
    "X = data.drop('Outcome', axis = 1)\n",
    "\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = X.columns\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "# 打印分割后的数据统计信息\n",
    "print(\"分割出来的训练集数据占比%0.1f%% (%d)\" % (len(X_train)/len(data.index)*100, len(X_train)))\n",
    "print(\"分割出来的测试集数据占比%0.1f%% (%d)\" % (len(X_test)/len(data.index)*100, len(X_test)))\n",
    "\n",
    "print(\"分割出来的训练集数据正负样本比率%0.1f%% : %0.1f%%\" % (len(y_train[y_train[:] == 1])/len(y_train) * 100,\n",
    "                                             len(y_train[y_train[:] == 0])/len(y_train) * 100))\n",
    "\n",
    "print(\"分割出来的测试集数据正负样本比率%0.1f%% : %0.1f%%\" % (len(y_test[y_test[:] == 1])/len(y_test) * 100,\n",
    "                                             len(y_test[y_test[:] == 0])/len(y_test) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据分割结果,\n",
    "* 1.分割比例符合题目要求\n",
    "* 2.训练和测试数据集中的正负样本比例与原数据比例基本一致\n",
    "\n",
    "分割结果有效"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、模型训练\n",
    "根据要求需要进行以下模型训练：\n",
    "* Logistic 回归，并选择最佳的正则函数（L1/L2）及正则参数\n",
    "* 线性 SVM，并选择最佳正则参数\n",
    "* RBF 核的 SVM，并选择最佳的超参数（正则参数、RBF 核函数宽度）；"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Logistic 回归\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The results by the LogisticRegression model：\n",
      "\n",
      "测试集上的accuracy_score为: 0.7532467532467533\n",
      "预测集上的recall_score为: 0.5636363636363636\n"
     ]
    }
   ],
   "source": [
    "# 导入库包\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression(n_jobs=1)\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss',n_jobs=1)\n",
    "grid.fit(X_train,y_train)\n",
    "\n",
    "LR_PredictTest = grid.predict(X_test)\n",
    "\n",
    "print(\"The results by the LogisticRegression model：\\n\")\n",
    "print(\"测试集上的accuracy_score为: {}\".format(metrics.accuracy_score(y_test, LR_PredictTest)))\n",
    "print(\"预测集上的recall_score为: {}\".format(metrics.recall_score(y_test, LR_PredictTest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fei/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/fei/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/fei/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/fei/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/fei/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/fei/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/fei/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00059381, 0.00058231, 0.00043001, 0.000563  , 0.00063887,\n",
       "        0.00064468, 0.00067015, 0.00067248, 0.00065794, 0.00065637,\n",
       "        0.00068336, 0.00066562, 0.00066352, 0.00066791]),\n",
       " 'mean_score_time': array([0.00051351, 0.00041227, 0.00038514, 0.00038915, 0.00038157,\n",
       "        0.00038991, 0.00038829, 0.00037599, 0.00038629, 0.00038328,\n",
       "        0.00037355, 0.00037808, 0.00040092, 0.00038118]),\n",
       " 'mean_test_score': array([-0.69314718, -0.62271976, -0.66249056, -0.49427575, -0.45703815,\n",
       "        -0.45144908, -0.45221338, -0.452032  , -0.45258142, -0.45256621,\n",
       "        -0.45262846, -0.45262758, -0.45263543, -0.45263381]),\n",
       " 'mean_train_score': array([-0.69314718, -0.62202303, -0.66144442, -0.48985772, -0.44587689,\n",
       "        -0.43902526, -0.43582832, -0.43570387, -0.43565125, -0.43564993,\n",
       "        -0.43564936, -0.43564935, -0.43564935, -0.43564934]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n",
       "                    100, 1000, 1000],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_penalty': masked_array(data=['l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1',\n",
       "                    'l2', 'l1', 'l2', 'l1', 'l2'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([14, 12, 13, 11, 10,  1,  3,  2,  5,  4,  7,  6,  9,  8],\n",
       "       dtype=int32),\n",
       " 'split0_test_score': array([-0.69314718, -0.62367897, -0.66160614, -0.49609206, -0.46868244,\n",
       "        -0.45595374, -0.45889269, -0.45754968, -0.45832249, -0.45820601,\n",
       "        -0.45829465, -0.45827958, -0.45828879, -0.45828703]),\n",
       " 'split0_train_score': array([-0.69314718, -0.62184657, -0.65238451, -0.48914774, -0.44378516,\n",
       "        -0.43733706, -0.43406981, -0.43394088, -0.43388714, -0.43388586,\n",
       "        -0.43388528, -0.43388527, -0.43388526, -0.43388526]),\n",
       " 'split1_test_score': array([-0.69314718, -0.62432978, -0.6625148 , -0.495471  , -0.44567027,\n",
       "        -0.44119465, -0.43579189, -0.43527368, -0.43489184, -0.43481314,\n",
       "        -0.43477073, -0.43476951, -0.43477087, -0.43476517]),\n",
       " 'split1_train_score': array([-0.69314718, -0.62133617, -0.6641926 , -0.4910143 , -0.45046777,\n",
       "        -0.44387986, -0.4411819 , -0.44108104, -0.44103907, -0.44103773,\n",
       "        -0.44103729, -0.44103727, -0.44103727, -0.44103727]),\n",
       " 'split2_test_score': array([-0.69314718, -0.62352153, -0.66023683, -0.50426414, -0.48176786,\n",
       "        -0.48398969, -0.49403908, -0.49521703, -0.49722056, -0.49734262,\n",
       "        -0.49756203, -0.4975718 , -0.49759908, -0.49759489]),\n",
       " 'split2_train_score': array([-0.69314718, -0.62136174, -0.65452615, -0.48459094, -0.43660992,\n",
       "        -0.42888112, -0.42506795, -0.42493227, -0.42486687, -0.42486548,\n",
       "        -0.42486477, -0.42486476, -0.42486476, -0.42486475]),\n",
       " 'split3_test_score': array([-0.69314718, -0.61790605, -0.66580706, -0.47152695, -0.42703955,\n",
       "        -0.412056  , -0.40922322, -0.40784184, -0.40785873, -0.40772311,\n",
       "        -0.40772604, -0.40771644, -0.40771904, -0.40771582]),\n",
       " 'split3_train_score': array([-0.69314718, -0.62469922, -0.67101466, -0.49833006, -0.45580173,\n",
       "        -0.44959654, -0.4465613 , -0.44644282, -0.44639331, -0.44639213,\n",
       "        -0.4463916 , -0.44639159, -0.44639158, -0.44639158]),\n",
       " 'split4_test_score': array([-0.69314718, -0.62412699, -0.66232073, -0.50390317, -0.46173021,\n",
       "        -0.46379483, -0.46280228, -0.46397069, -0.46429844, -0.46443219,\n",
       "        -0.46447403, -0.46448591, -0.46448456, -0.46449137]),\n",
       " 'split4_train_score': array([-0.69314718, -0.62087148, -0.66510418, -0.48620555, -0.44271988,\n",
       "        -0.43543171, -0.43226062, -0.43212237, -0.43206988, -0.43206844,\n",
       "        -0.43206787, -0.43206786, -0.43206785, -0.43206785]),\n",
       " 'std_fit_time': array([2.39445031e-04, 4.90383840e-05, 1.71777898e-05, 7.99410065e-06,\n",
       "        1.70562459e-05, 1.42469814e-05, 2.96182287e-05, 9.80083073e-06,\n",
       "        1.52659399e-05, 1.14291809e-05, 2.29543485e-05, 1.42919162e-05,\n",
       "        3.49284439e-05, 1.99033681e-05]),\n",
       " 'std_score_time': array([1.71796653e-04, 4.74586899e-05, 1.04856480e-05, 1.82637969e-05,\n",
       "        1.13719414e-05, 1.28282490e-05, 9.36836372e-07, 6.80391654e-06,\n",
       "        8.60212119e-06, 8.31749448e-06, 2.91378329e-06, 7.65824822e-06,\n",
       "        1.73273727e-05, 1.92144667e-05]),\n",
       " 'std_test_score': array([0.        , 0.00241485, 0.00183489, 0.01192403, 0.01894642,\n",
       "        0.02399817, 0.0283369 , 0.02919478, 0.02988677, 0.02997894,\n",
       "        0.03005544, 0.03006172, 0.0300692 , 0.03007004]),\n",
       " 'std_train_score': array([0.        , 0.00137319, 0.00696391, 0.00478956, 0.00663019,\n",
       "        0.00712722, 0.00742245, 0.00743238, 0.00743857, 0.00743864,\n",
       "        0.00743871, 0.00743871, 0.00743871, 0.00743871])}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the complete results (list of named tuples)\n",
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4514490845328668\n",
      "{'C': 0.1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fei/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/home/fei/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "#pd.DataFrame(grid.cv_results_).to_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#cvresult = pd.DataFrame.from_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#test_means = cv_results['mean_test_score']\n",
    "#test_stds = cv_results['std_test_score'] \n",
    "#train_means = cvresult['mean_train_score']\n",
    "#train_stds = cvresult['std_train_score'] \n",
    "\n",
    "\n",
    "# plot CV误差曲线\n",
    "test_means = -grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = -grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = -grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = -grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'neg-logloss' )\n",
    "pyplot.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最优点：\n",
    "上图给出了L1正则和L2正则下、不同正则参数C对应的模型在训练集上测试集上的正确率（score）。\n",
    "可以看出在训练集上C越大（正则越少）的模型性能略好（不是很明显）；在测试集上当C=0.1时性能最好（L1正则和L2正则均是）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 线性SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LinearSVC默认参数\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "\n",
    "SVC1 = LinearSVC().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.86      0.81        99\n",
      "          1       0.67      0.53      0.59        55\n",
      "\n",
      "avg / total       0.73      0.74      0.73       154\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[85 14]\n",
      " [26 29]]\n"
     ]
    }
   ],
   "source": [
    "#在测试集上测试，估计模型性能\n",
    "y_predict = SVC1.predict(X_test)\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (SVC1, metrics.classification_report(y_test, y_predict)))\n",
    "print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 线性SVM正则参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集上利用SVC训练\n",
    "    SVC2 =  LinearSVC( C = C)\n",
    "#     class sklearn.svm.LinearSVC(penalty=’l2’, loss=’squared_hinge’, dual=True, tol=0.0001, \n",
    "#                                 C=1.0, multi_class=’ovr’, fit_intercept=True, intercept_scaling=1,\n",
    "#                                 class_weight=None, verbose=0, random_state=None, max_iter=1000)\n",
    "    SVC2 = SVC2.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC2.score(X_val, y_val)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7662337662337663\n",
      "accuracy: 0.7467532467532467\n",
      "accuracy: 0.7467532467532467\n",
      "accuracy: 0.7402597402597403\n",
      "accuracy: 0.7662337662337663\n",
      "accuracy: 0.487012987012987\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3, 3, 6)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "#penalty_s = ['l1','l2']\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "#    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train, y_train, X_val=X_test, y_val=y_test)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "    \n",
    "fig = pyplot.figure()\n",
    "x_axis = np.log10(C_s)\n",
    "#for j, penalty in enumerate(penalty_s):\n",
    "pyplot.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "    \n",
    "# pyplot.legend()\n",
    "\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "pyplot.savefig('SVM_Otto.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7662337662337663"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(accuracy_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结论：\n",
    "\n",
    "* logistics回归，预测集上的accuracy_score为: 0.7532467532467533\n",
    "\n",
    "* linearSVC分类，预测集上的accuracy_score为: 0.7662337662337663\n",
    "\n",
    "* linearSVC 分类获得的结果要好于 logistics 回归的结果。\n",
    "* linearSVC 重复运行会出现值波动，待查原因\n",
    "* c值大概在100左右\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 RBF 核的 SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC3 =  SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC3 = SVC3.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC3.score(X_val, y_val)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6428571428571429\n",
      "accuracy: 0.6493506493506493\n",
      "accuracy: 0.7662337662337663\n",
      "accuracy: 0.7792207792207793\n",
      "accuracy: 0.8116883116883117\n",
      "accuracy: 0.8181818181818182\n",
      "accuracy: 0.7597402597402597\n",
      "accuracy: 0.7792207792207793\n",
      "accuracy: 0.8116883116883117\n",
      "accuracy: 0.8506493506493507\n",
      "accuracy: 0.8571428571428571\n",
      "accuracy: 0.8636363636363636\n",
      "accuracy: 0.8116883116883117\n",
      "accuracy: 0.8571428571428571\n",
      "accuracy: 0.8441558441558441\n",
      "accuracy: 0.8571428571428571\n",
      "accuracy: 0.8506493506493507\n",
      "accuracy: 0.7987012987012987\n",
      "accuracy: 0.8506493506493507\n",
      "accuracy: 0.8441558441558441\n",
      "accuracy: 0.8441558441558441\n",
      "accuracy: 0.8571428571428571\n",
      "accuracy: 0.7987012987012987\n",
      "accuracy: 0.7207792207792207\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3, 3, 6)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-3, -1, 6)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_val=X_test, y_val=y_test)\n",
    "        accuracy_s.append(tmp)\n",
    "\n",
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    pyplot.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "pyplot.savefig('RBF_SVM_Otto.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8636363636363636"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(accuracy_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果：\n",
    "精度值为0.8636363636363636 ，比之前的两者回归模型都要好，而且多次运行精度都很稳定"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
